A fruit picking robot can automatically detect and pick fruits, and is widely used due to the advantages of high efficiency and high degree of automation. The picking action of the fruit picking robot depends on accurate detection and positioning of the fruit by the vision detection system of the fruit picking robot. The literature indicates that, the picking efficiency of the fruit can be improved by rotating and twisting the fruit in a specific manner with respect to the directions of the fruit and the stem. Therefore, in order to further improve the picking efficiency, it is necessary to improve the detection accuracy of the fruit picking robot on the fruit symmetry axis.
Currently, the common fruit symmetry axis detection methods include: a symmetry axis seeking method based on curvature change of a curved surface, a learning-based natural image detection method, a natural image symmetry axis detection method based on edge feature learning, and the like. However, many problems still exist, for example, the adopted three-dimensional point clouds need to be quite accurate, and the learning time becomes very long along with the increase of point cloud data, and it is not convenient to detect the fruit symmetry axis from images taken in natural scenes.